Top PDF Dynamic resource allocation in Cloud Computing using a new hybrid Metaheuristic algorithm

Dynamic resource allocation in Cloud Computing using a new hybrid Metaheuristic algorithm

Dynamic resource allocation in Cloud Computing using a new hybrid Metaheuristic algorithm

Ludwig et al.[73] developed a method to schedule tasks in grid computing. In the proposed algorithm, ants and tasks have strong relationship with each other. Whenever a task for allocation refers to the resource, an ant will arise to find the best machine to allocate that task. As soon as the task is allocated, the ant stores all information of relevant machines in the form of a sequence of pheromones in a Load information table. Load information table contains information on load in all machines. The ant meets machine and stores the load in the table to guide other ants to select the best possible route. Many stores are listed in the table. Authors added two rates to the proposed algorithm, Decay Rate (DR) and Mutation Rate (MR). These rates are used when the ant wants to go from one machine to another. There are two items to select, or go to a machine randomly due to the probability of mutation given. The second way is that, using Load information table in two machines, determines the next destination with the passage of time, due to higher decay probability, mutation probability is reduced. In this case, the ant relies on information in the table for routing, and does not do random selection.
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Survey on Dynamic Resource Allocation Strategy in Cloud Computing Environment

Survey on Dynamic Resource Allocation Strategy in Cloud Computing Environment

Abstract- Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques.
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Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

Technology for Women, Kadapa, Andhra Pradesh, India Abstract— Cloud computing is the practise epoch of technology which unifies everything into one. It is an on liking abets owing it offers busy compliant opinionated countenancing for true and determined usefulness in give up as-you-use manner to public. In Bovine computing compose depressing users underpinning request number of unsympathetic services simultaneously. Accordingly with respect to own be a application go off roughly aggressive are required open to requesting user in efficient manner to satisfy their need. In this arrangement a interpret of unalike policies for operative doctrinaire remittance in Reduce computing is shown based on Topology Discerning Means Tolerating (TARA), Direct Scheduling Strategy for Opinionated Allowance and Dynamic Resource Allocation for Parallel Data Processing. Additionally, value, stingy and catches of end Resource Allocation in Cloud computing systems is also discussed.
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Effective Load Balancing For Dynamic Resource Allocation in Cloud Computing.

Effective Load Balancing For Dynamic Resource Allocation in Cloud Computing.

Effective Load Balancing For Dynamic Resource Allocation in Cloud Computing. K Prasanna Kumar 1 , S.Arun Kumar 2 , Dr Jagadeeshan 3 M.Tech(CSE) Student, SRM University,Ramapuram,Chennai,Tamil Nadu,India 1 Assistant Professor, Department of IT, SRM University,Ramapuram, Chennai, Tamil Nadu,India 2 Head of the Department, Department of IT, SRM University,Ramapuram, Chennai, Tamil Nadu,India 3 Abstract — Resources are dynamic in nature so the load of resources varies with change in Configuration of cloud so the Load Balancing of the tasks in a cloud environment can significantly influence cloud’s performance. A poor scheduling policy may leave many processors idle while a clever one may consume an unduly large portion of the total CPU cycles. In the existing approach we face overhead issue of distributed dispatching of task to resource. In our proposed system our main goal of load balancing is to provide a distributed, low cost, scheme that balances the load across all the processors. To improve the global throughput of cloud resources, effective and efficient load balancing algorithms are fundamentally important. Various strategies, algorithms and policies have been proposed, implemented and classified for implementing Load balancing in Cloud computing environment. In this paper, we present a combination of algorithm called ACBLA with queue algorithm applied to efficiently schedule computation jobs among processing resources onto the cloud datacenters with less communication overhead.
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A Review- Dynamic Resource Allocation Using Virtual Machines For Cloud Computing Environment

A Review- Dynamic Resource Allocation Using Virtual Machines For Cloud Computing Environment

This paper presents the hypothetical study of heterogeneous dynamic resource allocation techniques in cloud computing environment. Cloud computing can resolve obscure set of tasks in shorter time by proper resource utilization. The proposed system multiplexes virtual to physical resources adaptively based on the changing needs. To use the skewness to merge VMs with different resource characteristics judiciously so that the capacities of servers are best utilized. The algorithm attains both overload avoidance and green computing for systems with multi- resource constraints.
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Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments

Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments

In this paper, we study the dynamic capacity control problem in a single provider scenario, with the goal of dynamic adjusting the capacity of VM services to maximize the total income based on time-varying aggregate demand from customers. In our previous work [6], we have presented a solution to this problem by periodically solving a static optimization problem. However, it is known that such my- opic solution (i.e. without consideration of the future) does not necessarily lead to an optimal solution over time. Fur- thermore, reconfiguration cost for supply adjustment have not been considered in our previous work. To address these limitations, in this paper, we present a solution using tech- niques from optimal control theory. Optimal control theory is a research field that specifically deals with optimization problems in dynamic settings. The standard techniques for solving this type of problems have been widely studied and used in many industries, including electricity spot markets. Specifically, we adopt the Model Predictive Control (MPC) approach to provide an online adaptive control mechanism that takes into account capacity constraints. In our approach, we first formulate the dynamic resource allocation problem as an optimal control problem. We then present an efficient solution for this problem using control theoretic techniques. Using simulations based on real cloud workloads, we show that our solution achieves high performance compared to existing solutions for this problem.
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Automated Negotiation with Decommitment for Dynamic Resource Allocation in Cloud Computing

Automated Negotiation with Decommitment for Dynamic Resource Allocation in Cloud Computing

this case, b must decide what time period to request resources since different sellers need to provide resources in the same time period and this decision making is difficult due to uncertainty and agents’ selfishness. In this work, b decides the task execution schedule for partial agreements based on its information about sellers’ available resources, which can be obtained from the bid messages and accep- tance messages from sellers. Note that there is no guarantee that b can get part or all of s’s available resources due to the market dynamics. Specifically, b searches from time max {t + σ, est(b)} until dl(b) and sets the task start time est as the earliest time point from which sellers’ available resources from time est to est + pd(b) can satisfy the buyer’s resource requirements. We use the parameter σ > 0 to allow the buyer the flexibility to negotiate for resources. We choose this simple rule for two reasons. First, since a buyer’s value of finish- ing a task generally decreases with the task start time, the buyer can potentially achieve a higher utility if it negotiates for a set of agree- ments with an early task start time. Second, due to market dynamics and agents’ strategic interaction, it is impossible to determine the best start time. If there is no start time for which the buyer’s resource requirements can be satisfied, the buyer simply sets est = est(b) and dl = dl(b) and it will not confirm any partial agreement. Us- ing our simple rule, we set the decommitment penalty in this case to pe = α · pr, where 0 < α < 0.2.
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An Expert System for Dynamic Resource Allocation in Hybrid Cloud Computing with the Specification of Proactive Workload Management

An Expert System for Dynamic Resource Allocation in Hybrid Cloud Computing with the Specification of Proactive Workload Management

In cloud platforms, resource allocation takes place at two levels. First, when an application is uploaded to the cloud, the load balancer allocates the requested instances to physical machines, attempting to balance the computational load of multiple application across physical computers. Second, when an application receives multiple incoming requests, these requests should be each allocated to a specific application instance to balance the computational load across a set of instances to balance the computational load across a set of instances of the same application.
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Dynamic Resource Allocation And Distributed Video Transcoding Using Hadoop Cloud Computing

Dynamic Resource Allocation And Distributed Video Transcoding Using Hadoop Cloud Computing

In order to process such large-scale video datasets, we are using the Hadoop MapReduce framework. I will dive further into the distributed video transcoder part of the framework that ingests the video into Hadoop, decodes the bit stream chunks in parallel and produces a sequence file (which is much more amenable for video analytics in Hadoop). Hadoop framework stores large files in a distributed file system (HDFS: Hadoop Distributed File System) as small chunks of certain block size (typically 64MB) across a cluster of commodity machines. Given this framework, when the large input file to be processed is a text file and is split into 64MB chunks, each Mapper process can access the lines in each split independently. However, when the input file is video file (bitstream) and is split into many chunks, each Mapper process needs to interpret the bitstream chunk appropriately to provide access to the individual decoded video frames for subsequent analysis. In the following section, we will describe how each of the splits (64MB chunks) of a video bitstream can be transcoded into a sequence of JPEG images that can be subsequently processed by video analytics MapReduce jobs.
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Optimal multi dimensional dynamic resource allocation in mobile cloud computing

Optimal multi dimensional dynamic resource allocation in mobile cloud computing

Moreover, extensive research such as in [20-22] has been done over wireless local area network (WLAN)/ cel- lular interworking mechanisms, which combines WLANs and cellular data networks into integrated wireless data networks featured with QoS capabilities. Liu et al. [23] suggest a new dynamic load balance (DLB) scheme to improve communication performance focusing on under- lying users. In their proposed scheme, joint session admis- sion control is a basis for user mobility, cognition, and service arrival awareness in integrated 3G/WLAN net- works. Gazis et al.and Luo et al. [24,25] recommend a standardization policy in the area of WLAN-cellular data network integration for different interworking architec- tures. Proposing the generic interworking architectures in the technical literature, [26] studies general aspects of integrated WLAN-cellular data networks. Access net- work discovery and selection function (ANDSF) suggests a function for selection of access network and control offloading amongst 3rd generation partnership project (3GPP) and other access networks. Such selections are
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Dynamic Resource Allocation using Virtual Machines for Cloud Computing
Mr N Srinivas & S Sathwik

Dynamic Resource Allocation using Virtual Machines for Cloud Computing Mr N Srinivas & S Sathwik

Cloud computing provides a ―computing-as a-service‖ model in which compute resources are made available as a utility service — an illusion of availability of as much resources (e.g., CPU, memory, and I/O) as demanded by the user. Moreover, users of cloud services pay only for the amount of resources (a ―pay- as-use‖ model) used by them. This model is quite different from earlier infrastructure models, where enterprises would invest huge amounts of money in building their own computing infrastructure. Typically, traditional data centers are provisioned to meet the peak demand, which results in wastage of resources during non-peak periods. To alleviate the above problem, modern-day data centers are shifting to the cloud. The important characteristics of cloud-based data centers are:
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Proposing Priority based Dynamic Resource Allocation [PDRA] Model in Cloud Computing

Proposing Priority based Dynamic Resource Allocation [PDRA] Model in Cloud Computing

experimental results show the proposed scheme can improve resource utilization and reduce the user usage cost [13]. Cloud computing is a new generation of computing based on virtualization technology. An important application on the cloud is the Database Management Systems (DBMSs). The work in this paper concerns about the Virtual Design Advisor (VDA). The VDA is considered a solution for the problem of optimizing the performance of DBMS instances running on virtual machines that share a common physical machine pool. It needs to calibrate the tuning parameters of the DBMS‟s query optimizer in order to operate in a what-if mode to accurately and quickly estimate the cost of database workloads running in virtual machines with varying resource allocation. The calibration process in the VDA had been done manually. This manual calibration process is considered a complex, time-consuming task because each time a DBMS has to run on a different server infrastructure or to replace with another on the same server, the calibration process potentially has to be repeated. According to the work in this paper, an Automatic Calibration Tool (ACT) has been introduced to automate the calibration process.
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Energy Efficient Dynamic Resource Allocation Technique in Mobile Cloud Computing

Energy Efficient Dynamic Resource Allocation Technique in Mobile Cloud Computing

Zhan et al. described about a novel adaptive scheme to interactively co-optimize the locality and utility of co- scheduled threads in thread-aware shared last level caches (SLLC) capacity management. In cloud environments, several techniques are being used that aim for energy efficiency. Although these techniques enable a reduction in power consumption, they usually impact application performance. Rossi et al. presented an orchestration of different energy saving techniques in order to improve the tradeoff between energy consumption and application performance [23]. Toosi et al. proposed a framework for reactive load balancing of web application requests among Geo-distributed sustainable data centers based on the availability of renewable energy sources on each site. The experiments demonstrate that our approach can reduce cost and brown energy usage with efficient utilization of green energy and without a priori knowledge of future workload [28].
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A System for Dynamic Resource Allocation Using Virtualization technology and supports green computing in cloud computing environment

A System for Dynamic Resource Allocation Using Virtualization technology and supports green computing in cloud computing environment

experience with traces collected for several Internet applications.) Now the predicted values are higher than the observed ones most of the time: 77 percent according. The median error is increased to 9.4 percent because we trade accuracy for safety. It is still quite acceptable nevertheless. So far we take as the last ob-served value. Most applications have their SLOs specified in terms of a cer-tain percentiles of requests meeting a specific performance level. More generally, we keep a window of W recently ob-served values and take as a high percentile of them. shows the result when W ¼ 8 and we take the 90% the percentile of the peak resource demand. The figure shows that the prediction gets substantially better .We have also investigated other pre-diction algorithms. Linear auto regression (AR) models, for example, are broadly adopted in load prediction by other .It models a predictive value as linear function of its past
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OCRP in Dynamic Resource Allocation Using
          Virtual Machines for Cloud Computing
          Environment

OCRP in Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

In Cloud computing environment the cost optimization problem draws significant of optimizing resource price and how to optimally provision cloud resources to meet service requirements. In Cloud Environment On Demand Cost, Reservation Cost and Expending Cost are the major areas to be used for finding the Optimal resource Cost. The stochastic programming (SPI) is used for finding the optimal resource cost under uncertainty. The Deterministic Equivalent Formulation (DEF) algorithm is used for solving linear mathematical optimization programming script errors is used to reduce the Cost in the On Demand. The Benders Decomposition algorithm is used for break down the optimization problems which they are reduced to many sub problems. It is used to reduce the on demand cost and reservation cost during the resource provisioning stage. The Sample Average Approximation (SAA) Algorithm can reduce the problem scenarios to obtain optimal resource provisioning cost. It is used to reduce the reservation cost and expending cost
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Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

based on the changing demand. We use the skewness metric to combine VMs with different resource characteristics appropriately so that the capacities of servers are well utilized. Our algorithm achieves both overload avoidance and green computing for systems with multi resource constraints. we propose a system that uses virtualization technology to allocate data center resources dynamically based on application needs and support green computing by optimizing the number of servers in use. We proposed the concept of “skewness” to measure the un-evenness in the multidimensional resource utilization of a server. By minimized skewness, we can combining different of workloads and improve the over-all utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulations and experimental results demonstrate that ours algorithm achieves good performance.
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A Novel Dynamic Resource Allocation Using Virtual Machines for Cloud Computing

A Novel Dynamic Resource Allocation Using Virtual Machines for Cloud Computing

We are trying to achieve two goals in our algorithm. Overload avoidance: The capacity of a PM should be sufficient to satisfy the resource needs of all VMs working on it. Otherwise the PM is overloaded and can lead to degraded performance of its VMs.Cloud Computing become a de facto standard for computing, infrastructure as a services has been emerged as an important paradigm in IT area. By applying this paradigm we can abstract the underlying physical resource such a CPUs, Memories and Storage and offer this Virtual Resource to users in the formal Virtual Machine. Multiple Virtual Machines are able to run on a unique physical machine. Multiple VMs are able to run on a unique Physical Machine (PM). Another important issues in Cloud computing is provisioning method for allocating resources to cloud consumers. Cloud computing environment consists of two provision. The goal is to achieve an optimal solution for provisioning resource which is the most critical part in cloud computing. To make an optimal decision the demand price and waiting-time uncertainties are taken into account to adjust the trade-offs between on-demand and oversubscribed costs.
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Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multi-dimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server
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An Approach for Dynamic Resource Allocation Using Virtualization technology for Cloud Computing

An Approach for Dynamic Resource Allocation Using Virtualization technology for Cloud Computing

VirtualMachineMonitor (VMM) is a computer for example Xen machine gives a mechanism of mapping VMs to physical resources and this mapping is largely put out of the way from the clients and where cloud users do not have knowledge of where their Virtual Machine instances run. Cloud service provider has to maintain the Physical Machine that has enough resources to fulfill cloud user’s needs. VM live migration is a technology, that used to change the mapping between VMs and PMs While applications are in running mode. However an insurance agreement question under discussion remains as how to come to a decision the mapping adjusting that the useable thing demands of VMs are met while the number of PMs used is made seem unimportant. It is hard when the useable thing needs of VMs are heterogeneous needing payment to the different put of applications they run and (make, become, be) different with time as the amount of work grow and get smaller. The capability of PMs can also be heterogeneous because many living-stages of computer and apparatus have Coexistence in a data inside.
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Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

The multiplexing of VMs to PMs is managed using the Usher framework [7]. The main logic of our system is implemented as a set of plug-ins to Usher. Each node runs an Usher local node manager (LNM) on domain 0 which collects the usage statistics of resources for each VM on that node. The CPU and network usage can be calculated by monitoring the scheduling events in Xen. The memory usage within a VM, however, is not visible to the hypervisor. One approach is to infer memory shortage of a VM by observing its swap activities [8]. Unfortunately, the guest OS is required to install a separate swap partition. Furthermore, it may be too late to adjust the memory allocation by the time swapping occurs. Instead we implemented a working set prober (WS Prober) on each hypervisor to estimate the working set sizes of VMs running on it. We use the random page sampling technique as in the VMware ESX Server [9].
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